RESEARCH PAPER
Convolutional Neural Network - Gated Recurrent Unit combined with Error Correction for Lithium Battery State of Health Estimation
1
School of Mechanical Engineering, Shandong Jianzhu University, China
Submission date: 2024-10-13
Final revision date: 2025-01-06
Acceptance date: 2025-02-22
Online publication date: 2025-02-22
Publication date: 2025-02-22
Corresponding author
Eksploatacja i Niezawodność – Maintenance and Reliability 2025;27(4):202184
HIGHLIGHTS
- HFs are extracted from partial charging data to enhance practical applicability.
- A CNN-GRU hybrid model is developed for precise SOH estimation of lithium-ion batteries.
- GPR-MC is employed to dynamically correct errors and optimize estimation accuracy.
KEYWORDS
TOPICS
ABSTRACT
To accurately estimate the State of Health (SOH) of lithium-ion batteries, this study proposes a novel approach combining a Convolutional Neural Network (CNN) and a Gated Recurrent Unit (GRU) with an error correction mechanism. By extracting health features from partial charging data, this method reduces dependence on complete charge-discharge cycles, addressing challenges like long data acquisition times and high costs. The CNN captures local features of battery degradation, while the GRU models aging dynamics to provide an initial SOH estimate. An error correction strategy using Gaussian Process Regression (GPR) and Markov Chain (MC) further refines the results. GPR models nonlinear relationships to optimize predictions, and MC adjusts error distributions dynamically. Experiments on the University of Maryland dataset demonstrate that this method achieves lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) than benchmark techniques, proving its accuracy and robustness.
ACKNOWLEDGEMENTS
The authors gratefully acknowledge the support provided by the Shandong Province Science and Technology Program for Small and Medium-Sized Enterprise Innovation Capacity Enhancement [Grant Nos. 2023TSGC0173 and 2023TSGC0185]. Additional support was received from the Shandong Province Undergraduate Teaching Reform Research Project, titled "Development of the 'Artificial Intelligence + X' Public Teaching Platform" [Grant No. M2020202].
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